Development of a knowledge graph framework - KOM120F - K1

Summary

This presentation discusses the development of OrthoKB, a knowledge framework designed to integrate genetic and transcriptomic data from multiple plant species, focusing on legumes and grains. It aims to facilitate comparative genomics and support translational research. The methods involve collecting and processing data, constructing a graph-based database using Neo4j, and testing the framework's functionality. Results include the successful creation of a multi-species database and the ability to query genes controlling specific traits. Challenges in data harmonization and standardization are discussed, along with future development plans to enhance interoperability and scalability for agricultural research.

Takeaways

• 😀 The OrtoKB framework was developed to facilitate translational research in plants, specifically focusing on legumes and grains.
• 😀 The main goal of the framework is to integrate genetic and transcriptomic data to enable complex comparative genomic analyses.
• 😀 The system uses Neo4j, a graph database, to allow for complex queries to identify genes related to specific traits across different species.
• 😀 The methodology includes the collection of plant genetic data, transforming it into a human-readable format, and constructing a multi-species database using Docker containers.
• 😀 OrtoKB helps researchers in generating new hypotheses by utilizing orthology relationships between genes from various species.
• 😀 A key feature of the OrtoKB framework is its ability to analyze the genetic and omic data of multiple species in a unified environment.
• 😀 The system supports translational research by aiding in the discovery and application of genetic information from one species to another.
• 😀 One of the challenges identified in the research is the harmonization of terminology, especially for QTL data across species.
• 😀 The framework is still under development, with plans to include additional omic data types, such as proteomics, to enhance its capabilities.
• 😀 For better interoperability with other databases, there is a need to standardize query languages like RDF, SPARQL, and adapt to newer formats like GraphQL.
• 😀 The system's complexity may be challenging for non-expert users, and improvements in the user interface are needed to make the platform more accessible.

Q & A

What is the primary goal of the research presented in the script?

The primary goal of the research is to develop a knowledge framework, called OrtoKB, which facilitates translational research by integrating genomic and transcriptomic data from various plant species, particularly legumes and grains. This framework aims to enable comparative analysis to identify genes controlling specific traits across species.

How does OrtoKB help in plant translational research?

OrtoKB helps by providing a unified environment to integrate genetic and omics data, allowing for complex queries to identify genes that regulate specific traits. This supports hypothesis generation, comparative research, and accelerates the application of genetic knowledge to different species, especially for agricultural purposes.

What methods were used to create the OrtoKB database?

The methods included data collection, processing to make the data human-readable, and creating the database using Neo4J. Data was structured using a functional and structural pipeline, integrating gene sequences and annotations. Tools like TrapID and MapMan were also used to analyze genetic data, and Docker containers were utilized for managing the database environment.

What challenges were identified during the development of OrtoKB?

The challenges identified include the harmonization of QTL terminology across species, managing gene expression data from multiple analysis methods, and ensuring interoperability with other existing databases. Additionally, there is the challenge of dealing with variations in genetic data across species.

How does Neo4J contribute to the OrtoKB framework?

Neo4J is used as the database platform for OrtoKB. It allows for complex queries to be run using Cypher, enabling the identification of specific genes across different species. Neo4J’s graph database structure is particularly well-suited for handling the complex relationships in genomic data.

What improvements are planned for OrtoKB in the future?

Future developments for OrtoKB include integrating additional omics data layers such as proteomics, improving query language standards for better interoperability, and enhancing the user interface to make the system more accessible to non-expert users.

What was the role of the test drives in the research?

Test drives were used to apply the OrtoKB framework to specific plant species, such as *Cicer arietinum* and *Vicia faba*, to validate the effectiveness of the database in identifying relevant genetic data and analyzing specific traits. The results from these test drives helped demonstrate the potential of the system.

What are the benefits of using OrtoKB for agricultural research?

OrtoKB provides a comprehensive platform for integrating and analyzing genetic data from various species, which can accelerate the discovery of genes responsible for important agricultural traits. It also supports comparative studies that can lead to innovations in crop improvement and agricultural practices.

How does OrtoKB address the issue of data integration from different species?

OrtoKB integrates heterogeneous data from different species by creating a unified framework that links genomic data and omics information. This integration allows for cross-species comparisons, enabling researchers to identify conserved genes and functional elements that control traits in different plant species.

What are the suggestions for improving the user experience with OrtoKB?

Suggestions for improving the user experience include creating a more user-friendly interface that can help non-expert users navigate the database. Additionally, enhancing the system’s accessibility and providing better documentation would allow a wider range of researchers to benefit from the platform.